Data Integration Overview

You need to consolidate data from different sources with varying formats, field names, and data types. This data must be shared and used by groups of people with differing roles for differing purpose and different needs. Most data integration platforms require you to consider the needs of these groups in advance and define data structures that satify everyone but isn’t a perfect fit for anyone.

With Flow Analytics configure-not-code workflow actions and generic data, you’re not tied to predetermined structures so you can consolidate data from any source including flat files, spreadsheets, databases, APIs, and more then provide it to any group in the structure and format they need.

Get Started With Flow Analytics Workflows

Completely free - no trial period or credit card required.

Fully functional - access all the core features of the Flow platform.

Develop custom workflows - use our advanced, configure-not-code development environment to build custom solutions.

Free help and support - full documentation and how-to videos along with free online, telephone or email support.

Example Data Integration Workflow

The Challenge

Workflow Actions
Explanation

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Data Integration Actions Overview

Action Type Description  
Import Files Description view actions →
Import Database Tables Description view actions →
Import API Data Description view actions →
Import Web Data Description view actions →
Import NoSQL Data Description view actions →
Denormalize Data Description view actions →
Perform Lookups Description view actions →
Append Datasets Description view actions →
Slice Datasets Description view actions →
Deduplicate Dataset Description view actions →
Detect Different Data Description view actions →
Database Wait Description view actions →
FTP Download Description view actions →
FTP Upload Description view actions →
Web Download Description view actions →
Web Upload Description view actions →
Remove and Rename Data Description view actions →
Load Data Description view actions →
Save Data Description view actions →
Export Data Description view actions →

Additional Data Integration Information & Resources

Blog Posts
Post Description  
A Powerful Way to Normalize / Flatten Any JSON Data for Analysis Flattening JSON data is often a difficult task. In this blog post, I demonstrate how to consume any JSON based data source into structured data for analysis. This blog post focuses on using the normalization adapter to automatically create relational tables from web and file-based JSON based sources. The technique outlined in this blog post will allow you to integrate tens of thousands of data sources on-demand and with no code. view post →
How to Import and Analyze Common File Data Sources In this blog post, I provide a worked example demonstrating how to import and analyze different types of file-based data sources. File-based data sources are ubiquitous in business data analytics. This blog post focuses on how to work with data stored in delimited files, Excel workbooks, and XML documents. I provide an overview of each of these three data source types as well as a detailed explanation of the challenges of working with XML. I demonstrate how Flow is capable of automatically normalizing any XML irrespective of hierarchical complexity into structured data sets for analysis. I explain the advantages of the Flow approach to XML over traditional alternatives such as XPath. I finish the example by demonstrating basic analytics against the consolidated data by constructing and computing across various hypercubes. view post →
Import and Analyze MS Access Data with Flow Analytics This blog post provides a worked example of how to import and analyze Microsoft Access Data. We learn how to use the Access Database integration interface to consume the sample northwind database into Flow. A step-by-step walkthrough is provided which details how to denormalize the various relational tables into a consolidated flattened set for analysis. We learn how to apply generic expressions to compute new data points on the fly. Finally, we learn how to leverage Flow's multidimensional analysis engine to compute hypercubes and summarize the data. view post →
Import and Analyze JSON data using Flow Analytics In this blog post, I provide a worked example demonstrating how to import and analyze data from JSON based sources. Flow allows for the consumption of JSON data into a tabular form for analysis without requiring any knowledge of structure or schema. I demonstrate how to leverage this functionality to read and flatten JSON from a web-based resource into a dataset. I then show how to apply transformations to the data by using the expression builder to calculate new data points on the fly. I show how to compute hypercubes against the flattened data and perform a simple language analysis, highlighting the ability to wrangle and analyze the data. Finally, I demonstrate how to export the transformed data to various file formats allowing us to persist the flattened set for use elsewhere. view post →
Videos
Sample Workflows
Articles and Links